Artificial intelligence is everywhere portfolio screeners, robo-advisors, trading bots, and glitzy pitch decks with claims of market-beating returns. That is why AI is so alluring as a panacea of investing success. Before you give your model your choices, or chatbot which seems to know their stuff, you need to know where AI can perform well and where it fails. To the point: AI(investing strategies) may make a useful tool, although it will not replace conducting your own research on a company, its financial history, and your own objectives.
I have detailed the key risks of relying on AI alone to make investment decisions below, synoptic of the regulatory environment and scholarly evidence, and concluded with a checklist investors can implement instead.
The attractiveness of AI – and why that is dangerous
AI systems are good at recognizing patterns, working with extensive data, and automation of low value tasks (scanning filings, extracting metrics, screening stocks). That is why they could be helpful in performing such activities as creating watchlists, performing scenario analysis, or signaling suspicious accounting items.
Financial markets are however noisy adaptive systems of human behavior, changed policies and once-in-a-generation events – none of which tend to obey the historical pattern. Models fit against past data can thus overfit, fail to generalize or fail when the conditions change (data drift). According to several recent reviews of machine-learning stock-prediction tasks, the various methods are improving, but with inconsistent outcomes among datasets, and are prone to overfitting and poor out-of-sample prediction.
Legal and regulatory facts: the SEC and FINRA are on guard
The regulators in the United States do not take algorithmic and AI-driven advice lightly. The Securities and Exchange Commission has issued guidance and risk notices on automated investment advice and robo-advisers, making it clear that online/advice platforms are not exempted of the adviser regulation, disclosure and supervision. It is also found that companies have been fined by the SEC due to exaggerating or falsifying their AI capacities, a situation that is known as AI washing, indicating that regulators are not prepared to tolerate companies claiming they have automated edges or accuracy.
Other industry regulators such as FINRA also identify governance, model-risk management, and supervision as one of the main areas that firms should take into consideration before using AI tools to service their clients. That would imply that the app that is recommended to buy off-the-shelf may not have undergone intensive validation.
The dangers involved in relying on AI as your investment advisor
1. Overfitting and retroactive bias.
Models fitted to past data can tend to capture noise rather than signals – they will give good backtests but bad live results. In the financial ML models, academic and industry reviews have cautioned against overfitting many times.
2. Black-swan and regime-change blindness.
AI has been trained on historic regimes which is weak to unprecedented developments (pandemics, unpredictable changes in rates policy, geopolitical shocks). Models do not often know when their assumptions have failed.
3. Inexplainable decision logic
Most contemporary models (deep nets, ensemble methods) are black boxes. When a model cannot answer the question of why it suggests a stock, you can hardly be in a position to determine whether that rationale is reasonable based on your risk profile.
4. Model risk and vendor claims
Certain companies exaggerate AI use, and the agencies have imposed fines on advisers due to deceptive advertising. And putting your money into vendor assertions will put you at risk of AI-washing.
5. Issues of governance and update
Models need constant monitoring, updating of data and recalibration. A one-time recommendation is not a silver bullet – but that is how retail users tend to use it. The regulators emphasize on the control of the supervision and the model-risk supervision in the firms when such tools are used.
The areas where AI can be put into use, but as an aid
With that being said, AI can be beneficial in an environment where it is applied in a responsible manner and is integrated with human judgment:
- Narrowing a universe (then pick verification) through automated screening.
- Earnings transcripts summarization Natural-language processing to identify abnormal language in filings.
- Stress-testing and analysis of a scenario, in which AI is able to perform a large number of permutations in a shorter period than a human.
The qualifier that is critical: these are not the final answers, they are only inputs.
Research it yourself: a how-to-checklist of an investor
To make better choices than take an automatic AI recommendation, use this checklist every time you think of a stock:
- Read the filings. Review the newest 10-K of the company and most recent 10-Q. Important things to look at are the trends in revenues and margins, the cash flow, debt, and the risk factors.
- Check earnings and guidance. Make comparisons between the recent earnings reports and management guidance and the analyst consensus and the claim of the AI.
- Examine the balance sheet. Is the company over-geared? What percentage of short-term debt?
- Look at valuation vs. peers. Price/sales in relation to similar companies, P = Earnings per share, E = total equity, D = dividends per share. Cheap in the present day is not cheap by accident.
- Research on liquidity and the price history. Volume, volatility and historical drawdowns inform you about whether you can get/get out and how the stock will perform during stress.
- Situation analysis of business moat and competition. Is revenue durable? Is technology defensible? Check ownership & governance. Insider and institutional ownership, board structure and red flags (audit problems, turnover of management).
- Read latest news and transcripts of earnings calls. AI summaries prove useful, however, read the original when it counts.
- Test your position size and your exit strategy. Determine in advance the amount of risk you are to take; on what terms you shall sell. Diversify & rebalance. Everest bet your farm not on the choice of one model.
Lesson to be learned: apply AI, but do not delegate your duty
AI will never be able to replace critical thinking, it is a strong tool. Regulators (SEC, FINRA) are anticipating that the firms who apply AI to handle model risk and make accurate disclosures are doing so, and they have penalized those who have not. Academic survey and industry reports also indicate that AI stock-prediction activities are still advancing, albeit on a shaky overfitting and regime-change basis. Practically, the most safe solution to individual investors is to take AI recommendations as something to explore, rather than a buy/sell recommendation.
Also Read: Stock Market News: Record Highs, Federal Reserve actions, and Stocks to Pick
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